Overview

Dataset statistics

Number of variables11
Number of observations414
Missing cells54
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.4 KiB
Average record size in memory80.2 B

Variable types

Categorical2
Numeric9

Warnings

name has a high cardinality: 328 distinct values High cardinality
height_px is highly correlated with width_pxHigh correlation
width_px is highly correlated with height_pxHigh correlation
back_camera_mpix has 8 (1.9%) missing values Missing
front_camera_mpix has 46 (11.1%) missing values Missing
name is uniformly distributed Uniform
flash_gb has 26 (6.3%) zeros Zeros

Reproduction

Analysis started2021-03-28 21:18:17.971383
Analysis finished2021-03-28 21:19:04.294410
Duration46.32 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct328
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Apple iPhone 11 64GB
 
7
Apple iPhone 6s 32GB
 
4
Huawei Y6p
 
4
Nokia 3310 Dual Sim
 
3
Huawei P40
 
3
Other values (323)
393 

Length

Max length58
Median length21
Mean length20.83333333
Min length8

Characters and Unicode

Total characters8625
Distinct characters64
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique264 ?
Unique (%)63.8%

Sample

1st rowXiaomi Mi 10T 6+128GB
2nd rowSamsung Galaxy M21
3rd rowSamsung Galaxy Note20
4th rowXiaomi Redmi 9 4+64
5th rowXiaomi Redmi Note 8 Pro 6/64GB
ValueCountFrequency (%)
Apple iPhone 11 64GB 7
 
1.7%
Apple iPhone 6s 32GB 4
 
1.0%
Huawei Y6p 4
 
1.0%
Nokia 3310 Dual Sim 3
 
0.7%
Huawei P40 3
 
0.7%
Meizu M8 4+64GB 3
 
0.7%
Apple iPhone 11 Pro Max 256GB 3
 
0.7%
Apple iPhone 6s Plus 32GB 3
 
0.7%
Apple iPhone 11 Pro 512GB 3
 
0.7%
Xiaomi Mi 10 Lite 5G 6+64 3
 
0.7%
Other values (318)378
91.3%
2021-03-28T23:19:05.058180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pro60
 
3.7%
apple53
 
3.3%
xiaomi53
 
3.3%
iphone53
 
3.3%
huawei45
 
2.8%
galaxy41
 
2.5%
samsung41
 
2.5%
redmi33
 
2.1%
nokia31
 
1.9%
motorola29
 
1.8%
Other values (350)1169
72.7%

Most occurring characters

ValueCountFrequency (%)
1563
 
18.1%
o496
 
5.8%
e418
 
4.8%
a407
 
4.7%
i378
 
4.4%
1275
 
3.2%
P266
 
3.1%
2253
 
2.9%
l228
 
2.6%
m225
 
2.6%
Other values (54)4116
47.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3565
41.3%
Uppercase Letter1871
21.7%
Space Separator1563
18.1%
Decimal Number1445
16.8%
Other Punctuation91
 
1.1%
Math Symbol67
 
0.8%
Dash Punctuation23
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
P266
14.2%
G220
11.8%
M192
10.3%
A155
 
8.3%
B149
 
8.0%
S139
 
7.4%
O86
 
4.6%
X84
 
4.5%
R78
 
4.2%
H77
 
4.1%
Other values (15)425
22.7%
ValueCountFrequency (%)
o496
13.9%
e418
11.7%
a407
11.4%
i378
10.6%
l228
 
6.4%
m225
 
6.3%
n200
 
5.6%
r173
 
4.9%
t157
 
4.4%
u153
 
4.3%
Other values (14)730
20.5%
ValueCountFrequency (%)
1275
19.0%
2253
17.5%
6174
12.0%
0156
10.8%
4154
10.7%
8124
8.6%
3104
 
7.2%
5101
 
7.0%
955
 
3.8%
749
 
3.4%
ValueCountFrequency (%)
/72
79.1%
.19
 
20.9%
ValueCountFrequency (%)
1563
100.0%
ValueCountFrequency (%)
+67
100.0%
ValueCountFrequency (%)
-23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5436
63.0%
Common3189
37.0%

Most frequent character per script

ValueCountFrequency (%)
o496
 
9.1%
e418
 
7.7%
a407
 
7.5%
i378
 
7.0%
P266
 
4.9%
l228
 
4.2%
m225
 
4.1%
G220
 
4.0%
n200
 
3.7%
M192
 
3.5%
Other values (39)2406
44.3%
ValueCountFrequency (%)
1563
49.0%
1275
 
8.6%
2253
 
7.9%
6174
 
5.5%
0156
 
4.9%
4154
 
4.8%
8124
 
3.9%
3104
 
3.3%
5101
 
3.2%
/72
 
2.3%
Other values (5)213
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII8625
100.0%

Most frequent character per block

ValueCountFrequency (%)
1563
 
18.1%
o496
 
5.8%
e418
 
4.8%
a407
 
4.7%
i378
 
4.4%
1275
 
3.2%
P266
 
3.1%
2253
 
2.9%
l228
 
2.6%
m225
 
2.6%
Other values (54)4116
47.7%

brand
Categorical

Distinct28
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Apple
53 
Xiaomi
53 
Huawei
45 
Samsung
41 
Nokia
31 
Other values (23)
191 

Length

Max length10
Median length6
Mean length5.903381643
Min length3

Characters and Unicode

Total characters2444
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)1.9%

Sample

1st rowXiaomi
2nd rowSamsung
3rd rowSamsung
4th rowXiaomi
5th rowXiaomi
ValueCountFrequency (%)
Apple53
12.8%
Xiaomi53
12.8%
Huawei45
10.9%
Samsung41
9.9%
Nokia31
 
7.5%
Motorola29
 
7.0%
OPPO27
 
6.5%
Realme19
 
4.6%
myPhone19
 
4.6%
ALCATEL18
 
4.3%
Other values (18)79
19.1%
2021-03-28T23:19:05.890499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apple53
12.8%
xiaomi53
12.8%
huawei45
10.8%
samsung41
9.9%
nokia31
 
7.5%
motorola29
 
7.0%
oppo27
 
6.5%
realme19
 
4.6%
myphone19
 
4.6%
alcatel18
 
4.3%
Other values (19)80
19.3%

Most occurring characters

ValueCountFrequency (%)
o251
 
10.3%
a243
 
9.9%
i196
 
8.0%
e178
 
7.3%
m149
 
6.1%
l112
 
4.6%
p107
 
4.4%
u104
 
4.3%
n98
 
4.0%
A93
 
3.8%
Other values (29)913
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1788
73.2%
Uppercase Letter655
 
26.8%
Space Separator1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
o251
14.0%
a243
13.6%
i196
11.0%
e178
10.0%
m149
8.3%
l112
 
6.3%
p107
 
6.0%
u104
 
5.8%
n98
 
5.5%
s53
 
3.0%
Other values (12)297
16.6%
ValueCountFrequency (%)
A93
14.2%
P92
14.0%
O76
11.6%
H62
9.5%
M57
8.7%
X53
8.1%
S50
7.6%
L40
6.1%
N35
 
5.3%
C28
 
4.3%
Other values (6)69
10.5%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2443
> 99.9%
Common1
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
o251
 
10.3%
a243
 
9.9%
i196
 
8.0%
e178
 
7.3%
m149
 
6.1%
l112
 
4.6%
p107
 
4.4%
u104
 
4.3%
n98
 
4.0%
A93
 
3.8%
Other values (28)912
37.3%
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2444
100.0%

Most frequent character per block

ValueCountFrequency (%)
o251
 
10.3%
a243
 
9.9%
i196
 
8.0%
e178
 
7.3%
m149
 
6.1%
l112
 
4.6%
p107
 
4.4%
u104
 
4.3%
n98
 
4.0%
A93
 
3.8%
Other values (29)913
37.4%

back_camera_mpix
Real number (ℝ≥0)

MISSING

Distinct18
Distinct (%)4.4%
Missing8
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean27.13866995
Minimum0.3
Maximum108
Zeros0
Zeros (%)0.0%
Memory size3.3 KiB
2021-03-28T23:19:06.263012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile2
Q112
median13
Q348
95-th percentile64
Maximum108
Range107.7
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.49509972
Coefficient of variation (CV)0.8657424908
Kurtosis1.10436383
Mean27.13866995
Median Absolute Deviation (MAD)5
Skewness1.188801903
Sum11018.3
Variance552.0197108
MonotocityNot monotonic
2021-03-28T23:19:06.571552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1294
22.7%
4881
19.6%
1366
15.9%
6439
9.4%
1632
 
7.7%
227
 
6.5%
817
 
4.1%
0.311
 
2.7%
1089
 
2.2%
508
 
1.9%
Other values (8)22
 
5.3%
(Missing)8
 
1.9%
ValueCountFrequency (%)
0.311
 
2.7%
227
 
6.5%
52
 
0.5%
817
 
4.1%
1294
22.7%
ValueCountFrequency (%)
1089
 
2.2%
6439
9.4%
508
 
1.9%
4881
19.6%
406
 
1.4%

front_camera_mpix
Real number (ℝ≥0)

MISSING

Distinct14
Distinct (%)3.8%
Missing46
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean13.92663043
Minimum2
Maximum44
Zeros0
Zeros (%)0.0%
Memory size3.3 KiB
2021-03-28T23:19:06.982894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q18
median12
Q316
95-th percentile32
Maximum44
Range42
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.622418505
Coefficient of variation (CV)0.6191317092
Kurtosis0.2485811291
Mean13.92663043
Median Absolute Deviation (MAD)4
Skewness1.051262274
Sum5125
Variance74.34610088
MonotocityNot monotonic
2021-03-28T23:19:07.359574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
884
20.3%
1664
15.5%
551
12.3%
3241
9.9%
1231
 
7.5%
2021
 
5.1%
1317
 
4.1%
716
 
3.9%
1015
 
3.6%
2511
 
2.7%
Other values (4)17
 
4.1%
(Missing)46
11.1%
ValueCountFrequency (%)
26
 
1.4%
551
12.3%
716
 
3.9%
884
20.3%
1015
 
3.6%
ValueCountFrequency (%)
441
 
0.2%
401
 
0.2%
3241
9.9%
2511
 
2.7%
249
 
2.2%

battery_mAh
Real number (ℝ≥0)

Distinct83
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3566.384058
Minimum460
Maximum7000
Zeros0
Zeros (%)0.0%
Memory size3.3 KiB
2021-03-28T23:19:07.795361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum460
5-th percentile989.5
Q13000
median3969
Q34300
95-th percentile5020
Maximum7000
Range6540
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation1197.45637
Coefficient of variation (CV)0.3357620353
Kurtosis0.04463808313
Mean3566.384058
Median Absolute Deviation (MAD)864
Skewness-0.6765466996
Sum1476483
Variance1433901.758
MonotocityNot monotonic
2021-03-28T23:19:08.533165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400049
 
11.8%
500046
 
11.1%
300031
 
7.5%
450020
 
4.8%
430016
 
3.9%
502015
 
3.6%
340011
 
2.7%
420011
 
2.7%
311010
 
2.4%
35009
 
2.2%
Other values (73)196
47.3%
ValueCountFrequency (%)
4601
 
0.2%
6004
1.0%
7501
 
0.2%
8005
1.2%
9002
 
0.5%
ValueCountFrequency (%)
70001
 
0.2%
60002
 
0.5%
52607
1.7%
51601
 
0.2%
502015
3.6%

ram_gb
Real number (ℝ≥0)

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.248792271
Minimum0.5
Maximum12
Zeros0
Zeros (%)0.0%
Memory size3.3 KiB
2021-03-28T23:19:08.839210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q13
median4
Q36
95-th percentile8
Maximum12
Range11.5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.645236202
Coefficient of variation (CV)0.6225854394
Kurtosis0.8075398989
Mean4.248792271
Median Absolute Deviation (MAD)2
Skewness0.8681517674
Sum1759
Variance6.997274567
MonotocityNot monotonic
2021-03-28T23:19:09.104626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4124
30.0%
362
15.0%
661
14.7%
851
12.3%
0.546
 
11.1%
245
 
10.9%
1215
 
3.6%
110
 
2.4%
ValueCountFrequency (%)
0.546
 
11.1%
110
 
2.4%
245
 
10.9%
362
15.0%
4124
30.0%
ValueCountFrequency (%)
1215
 
3.6%
851
12.3%
661
14.7%
4124
30.0%
362
15.0%

flash_gb
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.76811594
Minimum0
Maximum512
Zeros26
Zeros (%)6.3%
Memory size3.3 KiB
2021-03-28T23:19:09.327314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q132
median64
Q3128
95-th percentile256
Maximum512
Range512
Interquartile range (IQR)96

Descriptive statistics

Standard deviation95.92528543
Coefficient of variation (CV)0.9712171232
Kurtosis5.614721217
Mean98.76811594
Median Absolute Deviation (MAD)58
Skewness2.048400254
Sum40890
Variance9201.660385
MonotocityNot monotonic
2021-03-28T23:19:09.598450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
128115
27.8%
64107
25.8%
3268
16.4%
25647
11.4%
1627
 
6.5%
026
 
6.3%
5129
 
2.2%
48
 
1.9%
85
 
1.2%
12
 
0.5%
ValueCountFrequency (%)
026
6.3%
12
 
0.5%
48
 
1.9%
85
 
1.2%
1627
6.5%
ValueCountFrequency (%)
5129
 
2.2%
25647
11.4%
128115
27.8%
64107
25.8%
3268
16.4%

diag
Real number (ℝ≥0)

Distinct61
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.718405797
Minimum1.44
Maximum7.6
Zeros0
Zeros (%)0.0%
Memory size3.3 KiB
2021-03-28T23:19:10.017825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.44
5-th percentile2.4
Q15.5
median6.22
Q36.5
95-th percentile6.67
Maximum7.6
Range6.16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.296981173
Coefficient of variation (CV)0.2268081734
Kurtosis2.55191268
Mean5.718405797
Median Absolute Deviation (MAD)0.35
Skewness-1.90149015
Sum2367.42
Variance1.682160164
MonotocityNot monotonic
2021-03-28T23:19:10.311033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.552
 
12.6%
6.126
 
6.3%
6.6723
 
5.6%
5.521
 
5.1%
6.421
 
5.1%
2.418
 
4.3%
6.317
 
4.1%
4.717
 
4.1%
5.815
 
3.6%
5.714
 
3.4%
Other values (51)190
45.9%
ValueCountFrequency (%)
1.441
 
0.2%
1.711
 
0.2%
1.774
1.0%
1.84
1.0%
2.24
1.0%
ValueCountFrequency (%)
7.61
0.2%
6.92
0.5%
6.811
0.2%
6.82
0.5%
6.761
0.2%

height_px
Real number (ℝ≥0)

HIGH CORRELATION

Distinct43
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1873.42029
Minimum128
Maximum3840
Zeros0
Zeros (%)0.0%
Memory size3.3 KiB
2021-03-28T23:19:10.579771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum128
5-th percentile320
Q11440
median2258
Q32400
95-th percentile2640
Maximum3840
Range3712
Interquartile range (IQR)960

Descriptive statistics

Standard deviation743.5353754
Coefficient of variation (CV)0.3968865819
Kurtosis0.2203847468
Mean1873.42029
Median Absolute Deviation (MAD)378
Skewness-0.7282722556
Sum775596
Variance552844.8544
MonotocityNot monotonic
2021-03-28T23:19:10.965245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
234071
17.1%
240065
15.7%
32033
 
8.0%
144029
 
7.0%
160026
 
6.3%
243618
 
4.3%
152017
 
4.1%
156016
 
3.9%
133415
 
3.6%
179212
 
2.9%
Other values (33)112
27.1%
ValueCountFrequency (%)
1281
 
0.2%
1608
 
1.9%
2204
 
1.0%
32033
8.0%
8542
 
0.5%
ValueCountFrequency (%)
38403
0.7%
32005
1.2%
31203
0.7%
30405
1.2%
27782
 
0.5%

width_px
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean890.7777778
Minimum120
Maximum2160
Zeros0
Zeros (%)0.0%
Memory size3.3 KiB
2021-03-28T23:19:11.320060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile240
Q1720
median1080
Q31080
95-th percentile1305
Maximum2160
Range2040
Interquartile range (IQR)360

Descriptive statistics

Standard deviation321.3901085
Coefficient of variation (CV)0.360797178
Kurtosis0.6754649793
Mean890.7777778
Median Absolute Deviation (MAD)183
Skewness-0.5512068371
Sum368782
Variance103291.6018
MonotocityNot monotonic
2021-03-28T23:19:11.590553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1080184
44.4%
720102
24.6%
24033
 
8.0%
112518
 
4.3%
144016
 
3.9%
75015
 
3.6%
82812
 
2.9%
4806
 
1.4%
1285
 
1.2%
1764
 
1.0%
Other values (10)19
 
4.6%
ValueCountFrequency (%)
1204
 
1.0%
1285
 
1.2%
1764
 
1.0%
24033
8.0%
4806
 
1.4%
ValueCountFrequency (%)
21601
 
0.2%
17681
 
0.2%
16442
 
0.5%
144016
3.9%
13441
 
0.2%

price
Real number (ℝ≥0)

Distinct164
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1597.401498
Minimum39.99
Maximum8799
Zeros0
Zeros (%)0.0%
Memory size3.3 KiB
2021-03-28T23:19:12.002582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum39.99
5-th percentile129.99
Q1504
median899
Q32199
95-th percentile5334
Maximum8799
Range8759.01
Interquartile range (IQR)1695

Descriptive statistics

Standard deviation1598.001647
Coefficient of variation (CV)1.000375704
Kurtosis2.472568282
Mean1597.401498
Median Absolute Deviation (MAD)525
Skewness1.665963813
Sum661324.22
Variance2553609.264
MonotocityNot monotonic
2021-03-28T23:19:12.273115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69922
 
5.3%
89919
 
4.6%
39918
 
4.3%
49916
 
3.9%
109912
 
2.9%
79912
 
2.9%
59910
 
2.4%
149910
 
2.4%
9999
 
2.2%
19998
 
1.9%
Other values (154)278
67.1%
ValueCountFrequency (%)
39.991
0.2%
44.991
0.2%
491
0.2%
63.491
0.2%
691
0.2%
ValueCountFrequency (%)
87991
0.2%
71991
0.2%
69891
0.2%
67991
0.2%
66991
0.2%

Interactions

2021-03-28T23:18:23.546679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:24.501445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:25.480835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:26.438081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:27.372454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:28.203180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:28.795850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:29.243590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:29.584141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:29.973492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:30.428434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:30.850784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:31.464452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:32.087152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:32.867071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:33.332310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:33.727507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:34.138232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:34.819381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:35.553505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:36.459848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:36.885096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:37.464714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:37.980877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:38.397468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:38.845203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:39.415883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:40.008028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:40.583153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:41.229795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:41.831890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:42.361892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:42.971840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:43.716851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:44.579025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:45.481643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:46.267788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:46.936528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:47.682512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:48.489850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:49.012192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:49.503203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:50.041301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:50.528234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:51.045907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:51.622361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:52.110325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:52.528010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:52.895824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:53.417813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:53.820150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:54.214774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:54.616634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:55.078227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:55.418985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:56.092749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:56.592821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:56.856268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:57.142910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:57.550061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:57.899596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:58.291626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:58.635058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:58.966219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:59.226561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:59.485752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:18:59.749543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:19:00.067714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:19:00.385275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:19:00.629950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:19:00.880604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-28T23:19:01.287378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-03-28T23:19:12.508199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-28T23:19:13.159970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-28T23:19:14.003501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-28T23:19:15.089623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-03-28T23:19:01.903970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-28T23:19:02.751399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-28T23:19:03.492904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-28T23:19:03.987710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

namebrandback_camera_mpixfront_camera_mpixbattery_mAhram_gbflash_gbdiagheight_pxwidth_pxprice
0Xiaomi Mi 10T 6+128GBXiaomi64.020.050006.01286.67240010801999.0
1Samsung Galaxy M21Samsung48.020.060004.0646.4023401080899.0
2Samsung Galaxy Note20Samsung64.010.043008.02566.70240010803749.0
3Xiaomi Redmi 9 4+64Xiaomi13.08.050204.0646.5323401080619.0
4Xiaomi Redmi Note 8 Pro 6/64GBXiaomi64.020.045006.0646.5323401080769.0
5Samsung Galaxy M31sSamsung64.032.060006.01286.50234010801399.0
6Samsung Galaxy A21sSamsung48.013.050003.0326.501600720699.0
7Xiaomi Redmi Note 9 4+128Xiaomi48.013.050204.01286.5323401080799.0
8Motorola Moto E7 Plus 4/64GBMotorola48.08.050004.0646.501600720599.0
9Xiaomi Redmi 9C 2/32GBXiaomi13.05.050002.0326.531600720480.0

Last rows

namebrandback_camera_mpixfront_camera_mpixbattery_mAhram_gbflash_gbdiagheight_pxwidth_pxprice
404Getnord OnyxGetnord8.05.040002.0164.701280720699.00
405Maxcom MM136MaxcomNaNNaN6000.502.4032024069.00
406Motorola Moto E4 Plus DS 3GB/16GBMotorola13.05.050003.0165.501280720769.00
407Motorola Moto G7 Plus 4/64GB DSMotorola16.012.030004.0646.2022701080949.00
408Apple iPhone 6s 128GBApple12.05.017152.01284.7013347502099.00
409Xiaomi Mi Note 10 Pro 8/256Xiaomi108.032.052608.02566.47234010803149.00
410Archos 55 Helium 4 SeasonsArchos8.02.027001.0165.501280720319.00
411Maxcom MM901 NeptunMaxcom0.3NaN14000.501.80160128169.99
412Meizu M6s 32GBMeizu16.08.030003.0325.701440720669.00
413Meizu M6s 32GBMeizu16.08.030003.0325.701440720699.00